AI Medical Compendium Journal:
Journal of chemical information and modeling

Showing 111 to 120 of 934 articles

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.

Journal of chemical information and modeling
Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. However, translation of metabolomics MS methods to multiyear studies conducted across multiple batches i...

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Journal of chemical information and modeling
Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes is one of the key factors that is interlinked with a plethora of diseases. Health management can be a...

HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction.

Journal of chemical information and modeling
In recent years, natural language processing (NLP) techniques, including large language modeling (LLM), have contributed significantly to advancements in organic chemistry research. Chemical reaction representations provide a link between NLP models ...

Investigations into the Efficiency of Computer-Aided Synthesis Planning.

Journal of chemical information and modeling
The efficiency of machine learning (ML) models is crucial to minimize inference times and reduce the carbon footprints of models deployed in production environments. Current models employed in retrosynthesis to generate a synthesis route from a targe...

MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.

Journal of chemical information and modeling
Language models are transforming materials-aware natural-language processing by enabling the extraction of dynamic, context-rich information from unstructured text, thus, moving beyond the limitations of traditional information-extraction methods. Mo...

Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?

Journal of chemical information and modeling
Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods. In this study, we concentrated our efforts on a s...

MutualDTA: An Interpretable Drug-Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention.

Journal of chemical information and modeling
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing met...

DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism.

Journal of chemical information and modeling
Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspectiv...

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.

Journal of chemical information and modeling
In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-re...

An Automated Approach for Domain-Specific Knowledge Graph Generation─Graph Measures and Characterization.

Journal of chemical information and modeling
In 2020, nearly 3 million scientific and engineering papers were published worldwide (White, K. Publications Output: U.S. Trends And International Comparisons). The vastness of the literature that already exists, the increasing rate of appearance of ...